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bioRxiv · 10.1101/2020.05.06.081737

Improving enzyme optimum temperature prediction with resampling strategies and ensemble learning

Gado, J. E.·Beckham, G. T.·Payne, C. M.
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Abstract

Accurate prediction of the optimal catalytic temperature (Topt) of enzymes is vital in biotechnology, as enzymes with high Topt values are desired for enhanced reaction rates. Recently, a machine-learning method (TOME) for predicting Topt was developed. TOME was trained on a normally-distributed dataset with a median Topt of 37{degrees}C and less than five percent of Topt values above 85{degrees}C, limiting the methods predictive capabilities for thermostable enzymes. Due to the distribution of the training data, the mean squared error on Topt values greater than 85{degrees}C is nearly an order of magnitude higher than the error on values between 30 and 50{degrees}C. In this study, we apply ensemble learning and resampling strategies that tackle the data imbalance to significantly decrease the error on high Topt values (>85{degrees}C) by 60% and increase the overall R2 value from 0.527 to 0.632. The revised method, TOMER, and the resampling strategies applied in this work are freely available to other researchers as a Python package on GitHub.

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Gado, J. E., Beckham, G. T., Payne, C. M.. 2020-05-08. Improving enzyme optimum temperature prediction with resampling strategies and ensemble learning. https://doi.org/10.1101/2020.05.06.081737

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